Technical challenges of modelling real-life epidemics and examples of overcoming these
Technical challenges of modelling real-life epidemics and examples of overcoming these
The coronavirus disease 2019 (COVID-19) pandemic has highlighted the importance of mathematical modelling in informing and advising policy decision-making. Effective practice of mathematical modelling has challenges. These can be around the technical modelling framework and how different techniques are combined, the appropriate use of mathematical formalisms or computational languages to accurately capture the intended mechanism or process being studied, in transparency and robustness of models and numerical code, in simulating the appropriate scenarios via explicitly identifying underlying assumptions about the process in nature and simplifying approximations to facilitate modelling, in correctly quantifying the uncertainty of the model parameters and projections, in taking into account the variable quality of data sources, and applying established software engineering practices to avoid duplication of effort and ensure reproducibility of numerical results. Via a collection of 16 technical papers, this special issue aims to address some of these challenges alongside showcasing the usefulness of modelling as applied in this pandemic.
Panovska-Griffiths, J.
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Waites, W.
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Ackland, G.J.
8a650a6b-5ec2-4a72-8154-f96df0494e9a
3 October 2022
Panovska-Griffiths, J.
da117053-d638-4ccc-b527-d2e06e5bbb7a
Waites, W.
a069e5ff-f440-4b89-ae81-3b58c2ae2afd
Ackland, G.J.
8a650a6b-5ec2-4a72-8154-f96df0494e9a
Panovska-Griffiths, J., Waites, W. and Ackland, G.J.
(2022)
Technical challenges of modelling real-life epidemics and examples of overcoming these.
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380 (2233), [20220179].
(doi:10.1098/rsta.2022.0179).
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has highlighted the importance of mathematical modelling in informing and advising policy decision-making. Effective practice of mathematical modelling has challenges. These can be around the technical modelling framework and how different techniques are combined, the appropriate use of mathematical formalisms or computational languages to accurately capture the intended mechanism or process being studied, in transparency and robustness of models and numerical code, in simulating the appropriate scenarios via explicitly identifying underlying assumptions about the process in nature and simplifying approximations to facilitate modelling, in correctly quantifying the uncertainty of the model parameters and projections, in taking into account the variable quality of data sources, and applying established software engineering practices to avoid duplication of effort and ensure reproducibility of numerical results. Via a collection of 16 technical papers, this special issue aims to address some of these challenges alongside showcasing the usefulness of modelling as applied in this pandemic.
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panovska-griffiths-et-al-technical-challenges-of-modelling-real-life-epidemics-and-examples-of-overcoming-these
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Accepted/In Press date: 5 July 2022
e-pub ahead of print date: 15 August 2022
Published date: 3 October 2022
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Local EPrints ID: 500087
URI: http://eprints.soton.ac.uk/id/eprint/500087
ISSN: 1364-503X
PURE UUID: 6ed1bcd6-d73b-4226-a7a7-d7da6bfb5e8a
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Date deposited: 15 Apr 2025 16:54
Last modified: 22 Aug 2025 02:43
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Author:
J. Panovska-Griffiths
Author:
W. Waites
Author:
G.J. Ackland
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